What is Customer Segmentation
Customer Segmentation is a series of activities that aim to separate homogeneous groups of clients (retail or business) into sub-groups based on their behavior during the purchase. As a rule, each of the designated groups reacts differently to the product offered, thanks to which we have the opportunity to offer differently to each of them.
What do we expect from my analysis
In this case if we were the owner of an online shop. I store details about each order and transaction. I’d like to learn more about my customers and find out how can we attract them and encourage them to use our online shop in the future.
Our first idea is to find groups of similar customers based on shopping behaviour, then analyse each group separately and find out what is important for each user while making an order.
Kmeans Clustering
K-Means Clustering is an Unsupervised Learning algorithm, which groups the unlabeled dataset into different clusters. Here K defines the number of pre-defined clusters that need to be created in the process, as if K=2, there will be two clusters, and for K=3, there will be three clusters, and so on.
Elbow method
In cluster analysis, the elbow method is a heuristic used in determining the number of clusters in a data set. The method consists of plotting the explained variation as a function of the number of clusters, and picking the elbow of the curve as the number of clusters to use.
K-means clustering based on Country:
## K-means clustering with 3 clusters of sizes 31, 1, 5
##
## Cluster means:
## TotalQuantity Totalsales
## 1 -0.189686827 -0.189575299
## 2 5.904187629 5.904985452
## 3 -0.004779199 -0.005630236
##
## Clustering vector:
## [1] 3 1 1 1 1 1 1 1 1 1 3 1 1 3 3 1 1 1 1 1 1 1 1 3 1 1 1 1 1 1 1 1 1 1 2 1 1
##
## Within cluster sum of squares by cluster:
## [1] 0.01395158 0.00000000 0.02797449
## (between_SS / total_SS = 99.9 %)
##
## Available components:
##
## [1] "cluster" "centers" "totss" "withinss" "tot.withinss"
## [6] "betweenss" "size" "iter" "ifault"
The dataset was filtered according to the k-means clustering algorithm constraints. First we found the different unique countries available in the dataset. Totally 37 countries were present and some transactions.
We considered 3 different variables for clustering the market. They are frequency of the customer (how many times transactions happened from the particular country), total quantity purchased from the particular country and finally total amount of sales made by the particular country.
Initially number of clusters are considered as 3 by using Elbow method (The method consists of plotting the explained variation as a function of the number of clusters, and picking the elbow of the curve as the number of clusters to use.)
When we performed clustering using k-means clustering algorithm using 3 clusters as the input. It gave results as the sizes of 3 clusters are 32, 1 and 5. Among the available 38 countries 31 countries are grouped into cluster 1, only one country in cluster 2 and 5 countries in cluster 3.
Group no. 1 (green):
In group 1 we can see that the countries customers who are spending low amount on products are classified and the amount ranges from $ 548.4 - $ 99,000 and also this group has low range of customers range from 2 to 71 from each Countries no of Customers. Ex - Austria,Bahrain,Brazil,Switzerland etc. So We can classify the group as Impulsive/ One-Time Customers.
Group no. 2 (blue):
In group 2 we can see that the United Kingdom has high spending amount on products are classified and the highest amount of $ 6767873.394 also this group has high no of customers of Total 3950. So We can classify the group as Loyal Customers.
Group no. 3 (yellow):
In group 3 we can see that the 5 countries customers who are spending mid range amount on products are classified and the amount ranges from $ 100000 - $ 250285.22 and also this group has median range of customers range from 69 to 319 from each Countries no of Customers. Ex - Netherlands,Germany,France,Australia,EIRE etc. So We can classify the group as Focused Customers.
K-means clustering based on Products:
## K-means clustering with 3 clusters of sizes 23, 335, 3527
##
## Cluster means:
## TotalQuantity Totalsales
## 1 7.8412630 7.267834
## 2 1.7749716 1.636039
## 3 -0.2197234 -0.202788
##
## Clustering vector:
## [1] 2 3 3 3 3 3 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 2 3
## [38] 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [75] 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 2 2 3 2 3 3 3 2 3 3 3 3 3 3 3
## [112] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 2 2 3 2 2 3 3 3 3 3 3 3 3 3 3
## [149] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2
## [186] 2 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 1 3 3 2 3 3 3 3 1 3 3 3 3 2 3
## [223] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3
## [260] 3 3 2 2 3 3 2 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [297] 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [334] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 1 3 3 3
## [371] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [408] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3
## [445] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3
## [482] 3 3 3 2 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3
## [519] 2 2 3 2 3 3 3 3 2 3 3 3 3 3 3 2 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [556] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [593] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [630] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2
## [667] 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 2 3 2 2 3 3 3 3 3 3 3 3
## [704] 3 3 3 3 3 3 3 3 3 3 3 1 2 3 3 3 3 3 2 2 3 3 3 3 2 3 3 3 3 3 3 3 3 3 2 3 2
## [741] 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [778] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 2 3 3 3 3 3 3 3 3 3
## [815] 2 2 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [852] 3 3 2 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [889] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [926] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3
## [963] 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3
## [1000] 3 3 3 3 3 3 2 3 2 2 2 2 3 3 2 3 3 3 2 3 2 3 2 3 2 3 3 3 2 3 3 3 3 3 3 3 3
## [1037] 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [1074] 3 3 3 3 3 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [1111] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [1148] 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 2 3 3 3 3 3 3 3 3 3 3
## [1185] 3 3 3 3 2 3 2 3 2 2 2 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [1222] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [1259] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [1296] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [1333] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 3 3 3
## [1370] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 2 3 3 2 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3
## [1407] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [1444] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3
## [1481] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 2 3 3 3 3
## [1518] 3 3 3 3 3 3 2 3 2 3 3 3 3 2 3 3 3 3 3 3 3 3 2 3 3 3 2 2 2 2 3 2 2 2 3 3 3
## [1555] 3 3 3 3 3 3 3 3 3 3 2 3 3 2 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [1592] 3 3 3 3 3 3 3 2 3 3 3 3 3 3 2 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [1629] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 2 2 3 3 2 3 3 3 3 2
## [1666] 2 3 2 3 3 3 3 3 3 3 3 3 3 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3
## [1703] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3
## [1740] 3 3 3 3 2 2 3 3 3 2 2 2 2 3 2 3 2 2 2 1 2 1 2 3 2 1 2 2 3 2 2 2 2 2 2 3 3
## [1777] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 2 3 3 3 3 3
## [1814] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [1851] 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [1888] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 2 3
## [1925] 3 3 3 3 2 3 3 3 3 3 2 2 2 2 2 2 3 2 1 3 3 2 3 2 3 2 2 2 2 3 3 3 3 3 3 3 3
## [1962] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [1999] 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [2036] 3 3 3 3 3 3 3 3 3 3 3 3 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [2073] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [2110] 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3
## [2147] 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [2184] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [2221] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 1 3 3 3 3 2 3 3 3 2 2 3 3 3 3 2 3 3 3 2 3
## [2258] 3 3 3 3 3 3 3 3 3 2 3 1 2 1 2 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3
## [2295] 3 3 3 3 3 3 3 3 3 3 3 2 3 2 3 3 2 3 3 2 1 2 3 2 3 2 3 3 3 3 3 3 3 3 3 3 3
## [2332] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [2369] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 2 3 3 2 3 3 3 3 3 3 3 3 3
## [2406] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [2443] 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [2480] 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [2517] 3 3 3 3 3 3 3 3 2 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3
## [2554] 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 2 2 2 2 2 2 3 3 3 3 3 2 3 3 3 3 3 3 3 2 3
## [2591] 3 3 3 3 3 3 3 3 3 2 1 3 3 3 3 3 3 3 3 3 3 3 1 3 3 2 3 3 3 3 3 3 3 3 3 3 3
## [2628] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 3 2 3 3 3 3
## [2665] 3 3 2 2 3 2 3 3 3 3 3 3 3 3 2 3 3 1 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3
## [2702] 2 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 2 2 3 3 3 2 3 3 3 3 2 3 3 3
## [2739] 3 2 3 3 3 3 3 3 3 3 2 3 2 3 3 3 3 3 3 3 3 2 3 2 3 3 3 2 3 3 3 1 2 3 2 3 2
## [2776] 3 2 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 2
## [2813] 3 3 2 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [2850] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 2 3 3 3 3 3 3 3 3 2 3 2 3 3 3 3 3 3
## [2887] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 2
## [2924] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [2961] 2 3 3 3 3 2 3 3 3 2 3 2 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2
## [2998] 2 3 3 3 2 2 3 3 3 3 3 2 3 2 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [3035] 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [3072] 3 3 2 3 2 2 3 3 3 3 3 3 3 2 3 2 2 2 3 2 2 3 3 3 3 3 3 2 3 3 2 2 3 3 3 2 3
## [3109] 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3
## [3146] 3 3 3 3 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [3183] 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3
## [3220] 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 3 3 3 3 3 3 2 3 3 3 3 3 3
## [3257] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3
## [3294] 3 3 3 3 3 3 3 3 3 3 2 2 3 3 3 3 3 3 3 3 2 3 3 3 2 2 3 3 3 3 3 3 3 2 3 3 3
## [3331] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 2 3 3 3 2 3 3 3
## [3368] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3
## [3405] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [3442] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 3 3 3 2 3 3 3 3 3 2 3 3 3 3 3
## [3479] 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [3516] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 3 3 3 3 3 3 3 3 3 3 3 3
## [3553] 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 2 2 3 3 3 3 3 3 3 3 3 2 3
## [3590] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 2 3 3 2 3
## [3627] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [3664] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [3701] 3 3 3 3 3 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3
## [3738] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 2 2 3 3 3 3 3 3 3 3 2 3 2 3 2 3 2 3 2 2 3
## [3775] 2 3 2 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 1 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3
## [3812] 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3
## [3849] 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 2 3 3 3 3
##
## Within cluster sum of squares by cluster:
## [1] 1084.4260 1018.4943 768.6098
## (between_SS / total_SS = 63.0 %)
##
## Available components:
##
## [1] "cluster" "centers" "totss" "withinss" "tot.withinss"
## [6] "betweenss" "size" "iter" "ifault"
The dataset was filtered according to the k-means clustering algorithm constraints. First we found the different unique products available in the dataset. Totally 3885 products were present and some transactions.
We considered 3 different variables for clustering the market. They are frequency of the customer (how many times transactions happened from the particular Products), total quantity purchased from the particular customer and finally total amount of sales made by the products.
Initially number of clusters are considered as 3 by using Elbow method (The method consists of plotting the explained variation as a function of the number of clusters, and picking the elbow of the curve as the number of clusters to use.)
When we performed clustering using k-means clustering algorithm using 3 clusters as the input. It gave results as among 3885 products 3527 products are clustered in 3 and 335 products in cluster 1 , atlast 23 products in cluster 2.
Group no. 1 (green):
In group 1 we can see no of customers range from who are spending high on products whose amount range from $ 13000 - $ 100000. So this customers are more intresed in Art and House Decor items .We can classify them in regular custommers.
Group no. 2 (blue):
In group 2 we can see no of customers spend amount range from $ 5000 - $ 12500 on products. So this customers are more intresed in Kitchen, Dining & Bar Supplies and Lamps, Lighting & Ceiling Fans items .We can classify them in Normal customers.
Group no. 3 (yellow):
In group 3 we can see that customers who are spending low compared to other two classifed groups they amount spending range from $ 500 - $ 5000 they are intrested in most of products. We can classify them in the one time customers .
Conclusion
We were able to group our customers based on Country and purchase behaviour and we managed to detect factors for each group. The best way forward is to prepare specific interactions for each one.
Here are some ideas:
We can offer selected promotions for products from their groups of interest. We could periodically send the discount offers by email or show the message right after the user logs present in the data.
For one time customers might be encouraged to return if we inform them about new and/or unique products from our line. We could even include recommendations from the appropriate influencers and can offer some special offers to attract this kind of customers.